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The authors propose and test a theoretical framework that develops and analyzes precursors to firm acquisitions to determine if acquirers differ from other firms.

The authors use longitudinal, archival data from a sample of the largest firms in the global pharmaceutical industry from 1991 to 2012 with 1,327 firm-year observations.

The authors integrate prior research to show that the firm characteristics involving (1) R&D investment, (2) prior experience and (3) network centrality influence the likelihood that a firm will complete an acquisition.

In contrast to research focusing on the performance of acquiring firms, the authors show that firm characteristics predict acquisition activity by highlighting that acquiring firms differ from other firms. The authors also develop how network synergies can be realized by acquirers that have information advantages from more central network positions.

It would take billions of miles’ field road testing to demonstrate that the safety of automated vehicle is statistically significantly higher than the safety of human driving because that the accident of vehicle is rare event.

This paper proposes an accelerated testing method for automated vehicles safety evaluation based on improved importance sampling (IS) techniques. Taking the typical cut-in scenario as example, the proposed method extracts the critical variables of the scenario. Then, the distributions of critical variables are statistically fitted. The genetic algorithm is used to calculate the optimal IS parameters by solving an optimization problem. Considering the error of distribution fitting, the result is modified so that it can accurately reveal the safety benefits of automated vehicles in the real world.

Based on the naturalistic driving data in Shanghai, the proposed method is validated by simulation. The result shows that compared with the existing methods, the proposed method improves the test efficiency by 35 per cent, and the accuracy of accelerated test result is increased by 23 per cent.

This paper has three contributions. First, the genetic algorithm is used to calculate IS parameters, which improves the efficiency of test. Second, the result of test is modified by the error correction parameter, which improves the accuracy of test result. Third, typical high-risk cut-in scenarios in China are analyzed, and the proposed method is validated by simulation.